Abuse Research Engineer
Stripe · Remote from the US
About this role
## Abuse Research Engineer ### About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet. ### About the Team The Abuse Research Group (ARG) handles proactive threat hunting and adversary behavior analysis across Stripe products. Rather than reacting to alerts, the team maps end-to-end fraud and abuse paths, validates novel attack vectors, and identifies product conditions that enable fraud. ### What You'll Do As an Abuse Research Engineer, you will safeguard Stripe's financial ecosystem by proactively hunting for advanced threats, dissecting complex fraud vectors, and extracting actionable adversary intelligence. You will develop hypothesis-driven threat hunting operations and collaborate cross-functionally with Fraud Ops, Strategy, Risk, Onboarding, and Security teams. **Key Responsibilities:** • Proactive Threat Hunting & Kill Chain Analysis • FT3 Taxonomy application and enrichment • Threat Intelligence & Signal Expansion • Cross-Functional Advisories & Strategic Controls • Agentic Testing & Adversary Simulation ### Minimum Requirements • 5+ years in threat intelligence, threat hunting, or technical incident response • 5+ years analyzing large datasets using data analytics tools • B.S./M.S. in Computer Science, Cybersecurity, or equivalent experience • Expert proficiency in Python and SQL • Hands-on experience in log analysis, digital forensics, and cyber investigation • Strong communication skills for translating technical research into actionable recommendations ### Preferred Qualifications • Deep understanding of financial fraud TTPs (ATO, Card Testing, Credential Stuffing) • Familiarity with FT3 or MITRE ATT&CK taxonomies • Proficiency with Databricks, Trino, PySpark, Pandas, or Scikit-Learn • Experience with Threat Intelligence Platforms and OSINT • Background with agentic LLM tools or automated testing systems
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